Performance analysis of a caching algorithm for a catch-up television service

The catch-up TV (CUTV) service allows users to watch video content that was previously broadcast live on TV channels and later placed on an on-line video store. Upon a request from a user to watch a recently missed episode of his/her favourite TV series, the content is streamed from the video server to the customer's receiver device. This requires that an individual flow is set up for the duration of the video, and since it is hard to impossible to employ multicast streaming for this purpose (as users seldomly issue a request for the same episode at the same time), these flows are unicast. In this paper, we demonstrate that with the growing popularity of the CUTV service, the number of simultaneously running unicast flows on the aggregation parts of the network threaten to lead to an unwieldy increase in required bandwidth. Anticipating this problem and trying to alleviate it, the network operators deploy caches in strategic places in the network. We investigate the performance of such a caching strategy and the impact of its size and the cache update logic. We first analyse and model the evolution of video popularity over time based on traces we collected during 10 months. Through simulations we compare the performance of the traditional least-recently used and least-frequently used caching algorithms to our own algorithm. We also compare their performance with a "perfect" caching algorithm, which knows and hence does not have to estimate the video request rates. In the experimental data, we see that the video parameters from the popularity evolution law can be clustered. Therefore, we investigate theoretical models that can capture these clusters and we study the impact of clustering on the caching performance. Finally, some considerations on the optimal cache placement are presented

Distributional little's law for queues with heterogeneous server interruptions

Distributional forms of Little's law relate the steady-state distributions of the number of customers in a queueing system (system content) and the time a customer spends in the system (delay). A new law for discrete-time multiserver queues is discussed, with single-slot service times, a first-come-first-served discipline and heterogeneous server interruptions

Real-time media: spiegel van de zintuigen

‘Beauty lies in the eye of the beholder’. Bij de uitvinding van de lp en de videoband lieten ingenieurs zich in de eerste plaats leiden door hun kennis van fysica. De maatstaf voor de kwaliteit van audio of video was een set van technisch meetbare parameters. Deze aanpak heeft plaats geruimd voor zogeheten subjectieve maatstaven, die zo dicht mogelijk aansluiten bij de menselijke waarneming. In deze voordracht lichten we toe hoe de menselijke perceptie vormgegeven heeft aan hedendaagse real-time mediastandaarden, met toepassingen in online radio, video en telefonie

Decomposing SLAs for Network Slicing

In Press / En PrensaWhen a network slice is requested, multiple technology and/or administrative domains are invoked to ensure that the slice end-to-end service level agreement (SLA) is met. Therefore, this SLA requirement needs to be decomposed in portions that each of the domains can support. In this paper we consider a management architecture consisting of an end-to-end service orchestrator responsible for decomposing the SLA, and domain controllers that govern their respective domain. The orchestrator has no detailed knowledge of the state of the resources in each of the domains when the network slice is requested. The orchestrator is only aware of the responses of the domains to previous requests, and captures this knowledge in a risk model associated with each domain. In this study, we propose an approach for decomposing the end-to-end SLA based on the best current estimate of the risk models of all involved domains. We further describe how the risk model for a particular domain is determined (and updated) based on historical data.Work partially funded by the EU H2020 5GROWTH Project (grant no. 856709)

Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

A Closed Form Formula for Long-lived TCP Connections Throughput

In this paper, we study the variation of the throughput achieved by TCP resulting from both the individual behavior of a connection and the interactio- n with all other connections sharing the same link. In particular, we calculate the Tail Distribution Function (TDF) of the instantaneous throughput seen by one TCP connection in the Additive Increase Multiplicative Decrease (AIMD) framework. For the particular case that each TCP connection experiences the same Round Trip Time (RTT) and under the many user approximati- on we prove that this TDF is given by a closed-form formula that solely depends on the network parameters (number of sources, capacity and buffer size of the bottleneck link). This formula can then be used as a dimensioning tool, where throughput is guaranteed to each user to be «larger than a given value for at least a certain percentage of the time». In the context defined here, this formula plays the same role for the dimensioning of an IP router as the Erlang B formula does for the dimensioning of a PSTN switch

A chunk-based caching algorithm for streaming video

Session 05 : Streaming applicationsInternational audienceIt is customary nowadays that large web objects are cached somewhere close to the user. This saves traffic upstream of the cache and offers the users a better responsiveness. Caching algorithms typically rank the objects in some way and cache the top-ranked objects. In this paper we study a scenario in which a requested video is (instantaneously) streamed to the user and in which the video library is highly dynamic: new videos are frequently introduced, get popular, get consumed and fade away. Caching streaming videos differs from caching traditional web objects as the former are consumed as their information trickles in, while the latter have to be downloaded (almost) completely before they can be consumed. We develop a caching algorithm specifically for streaming video taking into account the dynamicity of the library. First we make sure that its ranking algorithm can follow the dynamicity of the library (better than traditional algorithms can). Second we segment each video in chunks and propose a new algorithm to rank these chunks. We compare the performance of caching based on this new ranking algorithm with traditional caching algorithms and show that chunking is most beneficial

Analysis of the Competition between Wired, DSL and Wireless TCP Flows in an Access Network

This paper analyzes the performance of a large population composed of several classes of long lived TCP flows experiencing packet losses due to random transmission errors and to congestion created by the sharing of a common tail-drop or AQM bottleneck router. Each class has a different transmission error rate. This setting is used to analyze the competition between wired and wireless users in an access network, where one class (the wired class) has no or small (like BER in DSL) transmission error losses whereas the other class has higher transmission error losses, or the competition between DSL flows using different coding schemes. We propose a natural and simple model for the joint throughput evolution of several classes of TCP flows under such a mix of losses. Two types of random transmission error losses are considered: one where losses are Poisson and independent of the rate of the flow, and one where the losses are still Poisson but with an intensity that is proportional to the rate of the source. We show that the large population model where the population tends to infinity has a threshold on the transmission error rate (given in closed form) above which there are no congestion losses at all in steady state, and below which the stationary state is a periodic congestion regime in which we compute both the mean value and the distribution of the rate obtained by each class of flow. We also show that the maximum mean value for the aggregated rate is achieved at the threshold. For the finite population model and models based on other classes of point processes, a sufficient condition is obtained for the existence of congestion times in the case of arbitrary transmission error point processes

HTTP Turbulence

In this paper, we consider a set of HTTP flows using TCP over a common drop-tail link to download files. After each download, a flow waits for a random think time before requesting the download of another file, whose size is also random. When a flow is active its throughput is increasing with time according to the additive increase rule, but if it suffers losses created when the total transmission rate of the flows exceeds the link rate, its transmission rate is decreased. The throughput obtained by a flow, and the consecutive time to download one file are then given as the consequence of the interaction of all the flows through their total transmission rate and the link's behavior. We study the mean-field model obtained by letting the number of flows go to infinity. This mean-field limit may have two stable regimes : one without congestion in the link, in which the density of transmission rate can be explicitly described, the other one with periodic congestion epochs, where the inter-congestion time can be characterized as the solution of a fixed point equation, that we compute numerically, leading to a density of transmission rate given by as the solution of a Fredholm equation. It is shown that for certain values of the parameters (more precisely when the link capacity per user is not significantly larger than the load per user), each of these two stable regimes can be reached depending on the initial condition. This phenomenon can be seen as an analogue of turbulence in fluid dynamics: for some initial conditions, the transfers progress in a fluid and interaction-less way; for others, the connections interact and slow down because of the resulting fluctuations, which in turn perpetuates interaction forever, in spite of the fact that the load per user is less than the capacity per user. We prove that this phenomenon is present in the Tahoe case and both the numerical method that we develop and simulations suggest that it is also be present in the Reno case. It translates into a bi-stability phenomenon for the finite population model within this range of parameters. This research was supported in part by the "Opération Stratégique Conjointe" Alcatel-INRIA entitled "End to End Analysis of IP Traffic"